A Technology Selection and Operation (TSO) optimisation model for distributed energy systems: Mathematical formulation and case study. (15th October 2016) - Cedillos Alvarado, Dagoberto; Acha, Salvador; Shah, Nilay; Markides, Christos N.
Highlights: A model for the optimal selection and operation of distributed energy systems. Decisions influenced by real-time price models of electricity. Considers projections for energy costs, carbon tax and grid carbon-intensity. Allows for different levels of time granularity for energy demands and prices. Abstract: This paper presents a model which simultaneously optimises the selection and operation of technologies for distributed energy systems in buildings. The Technology Selection and Operation (TSO) model enables a new approach for the optimal selection and operation of energy system technologies that encompasses whole life costing, carbon emissions as well as real-time energy prices and demands; thus, providing a more comprehensive result than current methods. Utilizing historic metered energy demands, projected energy prices and a portfolio of available technologies, the mathematical model simultaneously solves for an optimal technology selection and operational strategy for a determined building based on a preferred objective: minimizing cost and/or minimizing carbon emissions. The TSO is a comprehensive and novel techno-economic model, capable of providing decision makers an optimal selection from a portfolio of available energy technologies. The current portfolio of available technologies is composed of various combined heat and power (CHP) and organic Rankine cycle (ORC) units. The TSO model framework is data-driven and therefore presents a high level of flexibility with respect to time granularity, period of analysis and the technology portfolio. A case study depicts the capabilities of the model; optimisation results under different temporal arrangements and technology options are showcased. Overall, the TSO model provides meaningful insights that allow stakeholders to make technology investment decisions with greater assurance.